home_prices

House Prices - à la Kaggle

Ask a home buyer to describe their dream house, and they probably won’t begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition’s dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

File descriptions

train.csv - the training set  
test.csv - the test set  
data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here  
sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms  

Data fields

Here’s a brief version of what you’ll find in the data description file.

SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.  
MSSubClass: The building class  
MSZoning: The general zoning classification  
LotFrontage: Linear feet of street connected to property  
LotArea: Lot size in square feet  
Street: Type of road access  
Alley: Type of alley access  
LotShape: General shape of property  
LandContour: Flatness of the property  
Utilities: Type of utilities available  
LotConfig: Lot configuration  
LandSlope: Slope of property  
Neighborhood: Physical locations within Ames city limits  
Condition1: Proximity to main road or railroad  
Condition2: Proximity to main road or railroad (if a second is present)  
BldgType: Type of dwelling  
HouseStyle: Style of dwelling  
OverallQual: Overall material and finish quality  
OverallCond: Overall condition rating  
YearBuilt: Original construction date  
YearRemodAdd: Remodel date  
RoofStyle: Type of roof  
RoofMatl: Roof material  
Exterior1st: Exterior covering on house  
Exterior2nd: Exterior covering on house (if more than one material)  
MasVnrType: Masonry veneer type  
MasVnrArea: Masonry veneer area in square feet  
ExterQual: Exterior material quality  
ExterCond: Present condition of the material on the exterior  
Foundation: Type of foundation  
BsmtQual: Height of the basement  
BsmtCond: General condition of the basement  
BsmtExposure: Walkout or garden level basement walls  
BsmtFinType1: Quality of basement finished area  
BsmtFinSF1: Type 1 finished square feet  
BsmtFinType2: Quality of second finished area (if present)  
BsmtFinSF2: Type 2 finished square feet  
BsmtUnfSF: Unfinished square feet of basement area  
TotalBsmtSF: Total square feet of basement area  
Heating: Type of heating  
HeatingQC: Heating quality and condition  
CentralAir: Central air conditioning  
Electrical: Electrical system  
1stFlrSF: First Floor square feet  
2ndFlrSF: Second floor square feet  
LowQualFinSF: Low quality finished square feet (all floors)  
GrLivArea: Above grade (ground) living area square feet  
BsmtFullBath: Basement full bathrooms  
BsmtHalfBath: Basement half bathrooms  
FullBath: Full bathrooms above grade  
HalfBath: Half baths above grade  
Bedroom: Number of bedrooms above basement level  
Kitchen: Number of kitchens  
KitchenQual: Kitchen quality  
TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)  
Functional: Home functionality rating  
Fireplaces: Number of fireplaces  
FireplaceQu: Fireplace quality  
GarageType: Garage location  
GarageYrBlt: Year garage was built  
GarageFinish: Interior finish of the garage  
GarageCars: Size of garage in car capacity  
GarageArea: Size of garage in square feet  
GarageQual: Garage quality  
GarageCond: Garage condition  
PavedDrive: Paved driveway  
WoodDeckSF: Wood deck area in square feet  
OpenPorchSF: Open porch area in square feet  
EnclosedPorch: Enclosed porch area in square feet  
3SsnPorch: Three season porch area in square feet  
ScreenPorch: Screen porch area in square feet  
PoolArea: Pool area in square feet  
PoolQC: Pool quality  
Fence: Fence quality  
MiscFeature: Miscellaneous feature not covered in other categories  
MiscVal: $Value of miscellaneous feature  
MoSold: Month Sold  
YrSold: Year Sold  
SaleType: Type of sale  
SaleCondition: Condition of sale  

2. ANALYSIS 2: Build the most predictive model for sales prices of homes in all of Ames Iowa. This includes all neighborhoods. Your group is limited to only the techniques we have learned in 6371 (no random forests or other methods we have not yet covered.) Specifically, you should produce 4 models: one from forward selection, one from backwards elimination, one from stepwise selection and one that you build custom. The custom model could be one of the three preceding models or one that you build by adding or subtracting variables at your will. Generate an adjusted R2, CV Press and Kaggle Score for each of these models and clearly describe which model you feel is the best in terms of being able to predict future sale prices of homes in Ames, Iowa. In your paper, please include a table similar to the one below. The group with the lowest public Kaggle score will receive an extra 2 bonus points on the final exam!
   Hint: for this analysis, start with the included SAS code written by Bivin and you may want to revisit the YouTube video linked above.
   Quick note on Kaggle completion: `We only have one course under our belts so far (almost), but you can compete in this competition with the tools you have now (Top 40th percentile or less)! And, after your next course (6372) you will really be able to do well top 25th percentile (or less)! With these skills and the skills you pick up in Data Mining and Quantifying the World, you will be able to compete with anyone!

Predictive Models
Adjusted R2
CV PRESS
Kaggle Score
Forward
.89
1272
.721
Backward
.78
1590
.945
Stepwise
.81
2001
.888
CUSTOM
.87
900
.2345

NOTE 1: ALL ANALYSIS MUST BE DONE IN SAS and all code must be placed in the appendix. Part of the grading process will be to run the code and verify the Kaggle score for each group.

Note 2: An extra 2 points on the final exam will be awarded to the team with the model with the lowest (best) Kaggle Score. In the unlikely event of a tie we will split these points.

Deliverables:
Your group is to turn in a paper that is no more that 7 pages long (without the appendix). Please put your code in the appendix, but any graphs and tables in the body of the paper.

Sample Format
Required deliverables in the complete report. The format of your paper (headers, sections, etc) is flexible although should contain the following information:

Introduction

Data Description
(Where did the data come from? How big is it? How many observations? Where can we find out more? What are the specific variables that we need to know to understand with respect to your analysis?)

Analysis Question 1:
Restatement of Problem

Specify the Model  
   
Checking Assumptions   
    Residual Plots   
    Influential point analysis (Cook’s D and Leverage)  
    Make sure and address each assumption.  

Comparing Competing Models  
    adj R2    
    Interval CVPress    
  
Parameter Interpretation  
    Interpretation   
    Confidence Intervals   

Conclusion
A short summary of the analysis.

Analysis Question 2
Restatement of Problem

Model Selection
Type of Selection
Stepwise
Forward
Backward
CUSTOM

    Checking Assumptions   
        Residual Plots  
        Influential point analysis (Cook’s D and Leverage)  
        Make sure and address each assumption  

    Comparing Competing Models  
        Adj R2     
        Interval CVPress     
        Kaggle Score   

    Conclusion: A short summary of the analysis.    
  

Appendix
Well commented SAS Code for Analysis 1 and 2

Rubric:
Presentation (30%):
Organized paper with title, headings, subheadings, etc.
Labeled plots, figures, tables and charts.
Every plot, figure, table and chart included is referenced in the paper and vice versa.
No spelling or grammatical errors.
Analysis Question 1: (35%)
Analysis Question 2: (35 %)

rm(list=ls())

home_dir <- "~/_smu/_src/home_prices/"
setwd(home_dir)
data_dir <- "./data"
setwd(data_dir)

homes <- read.csv("train.csv", stringsAsFactors = FALSE)

setwd(home_dir)

names(homes) <- tolower(names(homes))

dates <- paste(homes$yrsold, sprintf("%02d", homes$mosold), "01")
homes$sale_date <- strptime(dates, "%Y %m %d")

homes$total_baths <- homes$fullbath + homes$halfbath


#pdf ("homes_train_plots.pdf", width = 10, height = 7)

par (mfrow = c (2,3))
for (i in 2:(length(homes)))
{
    if(class(homes[,i]) == "integer")
    {
        plot (homes[,i],
             col = homes[,length(homes)]+1,
             main = (names(homes[i])))
        boxplot(scale(homes[,i]))
        
        plot(homes$saleprice ~ homes[,i])
    }
}

## Warning in if (class(homes[, i]) == "integer") {: la condition a une
## longueur > 1 et seul le premier élément est utilisé

#dev.off()